35 research outputs found
ΠΡΠΎΠ±Π»Π΅ΠΌΠ° ΠΊΠΎΡΠ΅ΡΠ΅ΡΠ΅Π½ΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΊΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ
Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΏΡΠΈΠΊΠ»Π°Π΄Π½ΡΠ΅ Π·Π°Π΄Π°ΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ Π»Π΅ΡΠ΅Π±Π½ΠΎ-Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ°, ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠ² ΠΈ ΡΠΈΠ½Π΄ΡΠΎΠΌΠΎΠ² (Π½ΠΎΠ·ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΎΡΠΌ) ΠΊΠ°ΠΊ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² Π±Π°Π· Π΄Π°Π½Π½Ρ
Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events
We introduce a novel iterative approach for event coreference resolution that
gradually builds event clusters by exploiting inter-dependencies among event
mentions within the same chain as well as across event chains. Among event
mentions in the same chain, we distinguish within- and cross-document event
coreference links by using two distinct pairwise classifiers, trained
separately to capture differences in feature distributions of within- and
cross-document event clusters. Our event coreference approach alternates
between WD and CD clustering and combines arguments from both event clusters
after every merge, continuing till no more merge can be made. And then it
performs further merging between event chains that are both closely related to
a set of other chains of events. Experiments on the ECB+ corpus show that our
model outperforms state-of-the-art methods in joint task of WD and CD event
coreference resolution.Comment: EMNLP 201
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
Most successful information extraction systems operate with access to a large
collection of documents. In this work, we explore the task of acquiring and
incorporating external evidence to improve extraction accuracy in domains where
the amount of training data is scarce. This process entails issuing search
queries, extraction from new sources and reconciliation of extracted values,
which are repeated until sufficient evidence is collected. We approach the
problem using a reinforcement learning framework where our model learns to
select optimal actions based on contextual information. We employ a deep
Q-network, trained to optimize a reward function that reflects extraction
accuracy while penalizing extra effort. Our experiments on two databases -- of
shooting incidents, and food adulteration cases -- demonstrate that our system
significantly outperforms traditional extractors and a competitive
meta-classifier baseline.Comment: Appearing in EMNLP 2016 (12 pages incl. supplementary material
Neural Cross-Lingual Entity Linking
A major challenge in Entity Linking (EL) is making effective use of
contextual information to disambiguate mentions to Wikipedia that might refer
to different entities in different contexts. The problem exacerbates with
cross-lingual EL which involves linking mentions written in non-English
documents to entries in the English Wikipedia: to compare textual clues across
languages we need to compute similarity between textual fragments across
languages. In this paper, we propose a neural EL model that trains fine-grained
similarities and dissimilarities between the query and candidate document from
multiple perspectives, combined with convolution and tensor networks. Further,
we show that this English-trained system can be applied, in zero-shot learning,
to other languages by making surprisingly effective use of multi-lingual
embeddings. The proposed system has strong empirical evidence yielding
state-of-the-art results in English as well as cross-lingual: Spanish and
Chinese TAC 2015 datasets.Comment: Association for the Advancement of Artificial Intelligence (AAAI),
201